Data centers often include hundreds to thousands of servers that are configured to process large volumes of data. These data centers may deliver email messages, perform search queries, process retail and bank transactions, stream video and other media, and perform other computation-intensive and high demand computing tasks. Often, a volume of processing at data centers varies greatly over time, which creates periods where the data centers operate near a peak output and other periods where the data centers are underutilized and operate well below the peak output. For example, data centers may experience a lower volume demand late at night or very early in the morning when fewer users are interacting with the data centers.
Data centers are very expensive to build costing upwards of hundreds of millions of dollars, where the expense relates to a capacity of the data centers. Some data centers are designed with extra capacity that can accommodate a very high computing volume that is experienced during a peak time. However, often the data center operates below capacity and the extra capacity may be unused (idle). In addition, this approach of designing to maximum capacity may be very costly because peaks may be much larger than an average computing workload, and thus a large portion of the investment to create a data center may only be used reach the peak capacity that is infrequent. This may result in millions of dollars in stranded capacity that could have been better allocated to provide an overall lower computational cost.
A second approach is to design a data center to perform at or near a mean workload level and delay or otherwise underperform during peak processing times. However, this approach may lead to large data latency, stale data, disappointed users, and other disadvantageous consequences. For example, throttling of CPU's may be employed to limit power consumption but also limits throughput, which may have disadvantageous results.
Operating costs for data centers are also very expensive. An improvement in efficiency of the data centers may enable a substantial reduction in operational costs as compared to computing workload. For example, by making a data center twice as efficient, it may perform operations that previously required two data centers, and thus the improved data center may have a much lower operation cost than an operation cost of two lower efficiency data centers that have a comparable computing capacity.